[tab] data/eval/handcrafted_test_cases/handcrafted_unit_tests.jsonl (jsonl)
{"task_id": "eval_insert/0", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```"}, {"role": "user", "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_insert/1", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```"}, {"role": "user", "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n src/input_pipeline/validation_to_testcases.py
```"}, {"role": "user", "content": "<stdout>
     1  import tyro
     2  import os
     3  from dataclasses import dataclass
     4
     5
     6  @dataclass
     7  class Args:
     8      input_file: str = \"data/eval/val/validation.jsonl\"
     9      output_file: str = \"data/eval/val/validation_testcases.jsonl\"
    10      task_name: str = \"validation_set\"
    11
    12
    13  def convert_to_incremental_jsonl(line, output_file, task_name):
    14      data = json.loads(line)
    15
    16      system_prompt = data.get(\"system_prompt\", \"\")
    17      conversations = data.get(\"conversations\", [])
    18
    19      output_lines = []
    20      context = []
    21
    22      for i, conv in enumerate(conversations):
    23          # Convert \"Assistant\"/\"User\" to \"assistant\"/\"user\"
    24          role = conv[\"from\"].lower()
    25          content = conv[\"value\"]
    26
    27          # Add current message to context
    28          context.append({\"role\": role, \"content\": content})
    29
    30          if i > 0 and role == \"assistant\":
    31              task_entry = {
    32                  \"system_prompt\": system_prompt,
    33                  \"task_id\": f\"{task_name}/{i-1}\",
    34                  \"context\": context[:-1].copy(),
    35                  \"expected_final_response\": content,
    36              }
    37              output_lines.append(task_entry)
    38
    39      with open(output_file, \"a\") as f:
    40          for line in output_lines:
    41              f.write(json.dumps(line) + \"\
\")
    42
    43      print(f\"Converted {len(output_lines)} entries to {output_file}\")
    44
    45
    46  if __name__ == \"__main__\":
    47      args = tyro.cli(Args)
    48
    49      if os.path.exists(args.output_file):
    50          os.remove(args.output_file)
    51
    52      # iterate over jsonl file and convert to incremental jsonl
    53      conversation_id = 0
    54      with open(args.input_file, \"r\") as f:
    55          for line in f:
    56              task_name = f\"conversation_{conversation_id}/{args.task_name}\"
    57              convert_to_incremental_jsonl(line, args.output_file, task_name)
    58              conversation_id += 1
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_terminal_invocation/0", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```"}, {"role": "user", "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_terminal_invocation/1", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```"}, {"role": "user", "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n src/input_pipeline/validation_to_testcases.py
```"}, {"role": "user", "content": "<stdout>
     1  import tyro
     2  import os
     3  from dataclasses import dataclass
     4
     5
     6  @dataclass
     7  class Args:
     8      input_file: str = \"data/eval/val/validation.jsonl\"
     9      output_file: str = \"data/eval/val/validation_testcases.jsonl\"
    10      task_name: str = \"validation_set\"
    11
    12
    13  def convert_to_incremental_jsonl(line, output_file, task_name):
    14      data = json.loads(line)
    15
    16      system_prompt = data.get(\"system_prompt\", \"\")
    17      conversations = data.get(\"conversations\", [])
    18
    19      output_lines = []
    20      context = []
    21
    22      for i, conv in enumerate(conversations):
    23          # Convert \"Assistant\"/\"User\" to \"assistant\"/\"user\"
    24          role = conv[\"from\"].lower()
    25          content = conv[\"value\"]
    26
    27          # Add current message to context
    28          context.append({\"role\": role, \"content\": content})
    29
    30          if i > 0 and role == \"assistant\":
    31              task_entry = {
    32                  \"system_prompt\": system_prompt,
    33                  \"task_id\": f\"{task_name}/{i-1}\",
    34                  \"context\": context[:-1].copy(),
    35                  \"expected_final_response\": content,
    36              }
    37              output_lines.append(task_entry)
    38
    39      with open(output_file, \"a\") as f:
    40          for line in output_lines:
    41              f.write(json.dumps(line) + \"\
\")
    42
    43      print(f\"Converted {len(output_lines)} entries to {output_file}\")
    44
    45
    46  if __name__ == \"__main__\":
    47      args = tyro.cli(Args)
    48
    49      if os.path.exists(args.output_file):
    50          os.remove(args.output_file)
    51
    52      # iterate over jsonl file and convert to incremental jsonl
    53      conversation_id = 0
    54      with open(args.input_file, \"r\") as f:
    55          for line in f:
    56              task_name = f\"conversation_{conversation_id}/{args.task_name}\"
    57              convert_to_incremental_jsonl(line, args.output_file, task_name)
    58              conversation_id += 1
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_terminal_invocation/2", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```"}, {"role": "user", "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n src/input_pipeline/validation_to_testcases.py
```"}, {"role": "user", "content": "<stdout>
     1  import tyro
     2  import os
     3  from dataclasses import dataclass
     4
     5
     6  @dataclass
     7  class Args:
     8      input_file: str = \"data/eval/val/validation.jsonl\"
     9      output_file: str = \"data/eval/val/validation_testcases.jsonl\"
    10      task_name: str = \"validation_set\"
    11
    12
    13  def convert_to_incremental_jsonl(line, output_file, task_name):
    14      data = json.loads(line)
    15
    16      system_prompt = data.get(\"system_prompt\", \"\")
    17      conversations = data.get(\"conversations\", [])
    18
    19      output_lines = []
    20      context = []
    21
    22      for i, conv in enumerate(conversations):
    23          # Convert \"Assistant\"/\"User\" to \"assistant\"/\"user\"
    24          role = conv[\"from\"].lower()
    25          content = conv[\"value\"]
    26
    27          # Add current message to context
    28          context.append({\"role\": role, \"content\": content})
    29
    30          if i > 0 and role == \"assistant\":
    31              task_entry = {
    32                  \"system_prompt\": system_prompt,
    33                  \"task_id\": f\"{task_name}/{i-1}\",
    34                  \"context\": context[:-1].copy(),
    35                  \"expected_final_response\": content,
    36              }
    37              output_lines.append(task_entry)
    38
    39      with open(output_file, \"a\") as f:
    40          for line in output_lines:
    41              f.write(json.dumps(line) + \"\
\")
    42
    43      print(f\"Converted {len(output_lines)} entries to {output_file}\")
    44
    45
    46  if __name__ == \"__main__\":
    47      args = tyro.cli(Args)
    48
    49      if os.path.exists(args.output_file):
    50          os.remove(args.output_file)
    51
    52      # iterate over jsonl file and convert to incremental jsonl
    53      conversation_id = 0
    54      with open(args.input_file, \"r\") as f:
    55          for line in f:
    56              task_name = f\"conversation_{conversation_id}/{args.task_name}\"
    57              convert_to_incremental_jsonl(line, args.output_file, task_name)
    58              conversation_id += 1
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '1i\\import json' src/input_pipeline/validation_to_testcases.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/0", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/1", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/2", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/3", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '205,205d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/4", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '205,205d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '205,216p'
```"}, {"role": "user", "content": "<stdout>
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   209      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211
   212      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: gt shape: {gt.shape}\")
   214      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: recon shape: {recon.shape}\")
   216
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/5", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '205,205d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '205,216p'
```"}, {"role": "user", "content": "<stdout>
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   209      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211
   212      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: gt shape: {gt.shape}\")
   214      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: recon shape: {recon.shape}\")
   216
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '208,208d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/6", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '205,205d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '205,216p'
```"}, {"role": "user", "content": "<stdout>
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   209      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211
   212      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: gt shape: {gt.shape}\")
   214      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: recon shape: {recon.shape}\")
   216
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '208,208d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '209,209d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_delete/7", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
     1	from dataclasses import dataclass
     2	import time
     3	import os
     4	import optax
     5	
     6	import dm_pix as pix
     7	import einops
     8	import jax
     9	import jax.numpy as jnp
    10	import numpy as np
    11	import orbax.checkpoint as ocp
    12	from PIL import Image, ImageDraw
    13	import tyro
    14	from flax import nnx
    15	
    16	from jasmine.models.genie import GenieDiffusion
    17	from jasmine.utils.dataloader import get_dataloader
    18	
    19	
    20	@dataclass
    21	class Args:
    22	    # Experiment
    23	    seed: int = 0
    24	    seq_len: int = 16
    25	    image_channels: int = 3
    26	    image_height: int = 90
    27	    image_width: int = 160
    28	    data_dir: str = \"data/coinrun_episodes\"
    29	    checkpoint: str = \"\"
    30	    print_action_indices: bool = True
    31	    output_dir: str = \"gifs/\"
    32	    # Sampling
    33	    batch_size: int = 1
    34	    start_frame: int = 1
    35	    diffusion_denoise_steps: int = 64
    36	    diffusion_corrupt_context_factor: float = 0.1
    37	    # Tokenizer checkpoint
    38	    tokenizer_dim: int = 512
    39	    tokenizer_ffn_dim: int = 2048
    40	    latent_patch_dim: int = 32
    41	    num_patch_latents: int = 1024
    42	    patch_size: int = 16
    43	    tokenizer_num_blocks: int = 4
    44	    tokenizer_num_heads: int = 8
    45	    # LAM checkpoint
    46	    lam_dim: int = 512
    47	    lam_ffn_dim: int = 2048
    48	    latent_action_dim: int = 32
    49	    num_actions: int = 6
    50	    lam_patch_size: int = 16
    51	    lam_num_blocks: int = 4
    52	    lam_num_heads: int = 8
    53	    use_gt_actions: bool = False
    54	    # Dynamics checkpoint
    55	    dyna_dim: int = 512
    56	    dyna_ffn_dim: int = 2048
    57	    dyna_num_blocks: int = 6
    58	    dyna_num_heads: int = 8
    59	    param_dtype = jnp.float32
    60	    dtype = jnp.bfloat16
    61	    use_flash_attention: bool = True
    62	
    63	
    64	args = tyro.cli(Args)
    65	
    66	if __name__ == \"__main__\":
    67	    \"\"\"
    68	    Dimension keys:
    69	        B: batch size
    70	        T: number of input (conditioning) frames
    71	        N: number of patches per frame
    72	        S: sequence length
    73	        H: height
    74	        W: width
    75	        E: B * (S - 1)
    76	    \"\"\"
    77	    jax.distributed.initialize()
    78	
    79	    rng = jax.random.key(args.seed)
    80	
    81	    # --- Load Genie checkpoint ---
    82	    rngs = nnx.Rngs(rng)
    83	    genie = GenieDiffusion(
    84	        # Tokenizer
    85	        in_dim=args.image_channels,
    86	        tokenizer_dim=args.tokenizer_dim,
    87	        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88	        latent_patch_dim=args.latent_patch_dim,
    89	        num_patch_latents=args.num_patch_latents,
    90	        patch_size=args.patch_size,
    91	        tokenizer_num_blocks=args.tokenizer_num_blocks,
    92	        tokenizer_num_heads=args.tokenizer_num_heads,
    93	        # LAM
    94	        lam_dim=args.lam_dim,
    95	        lam_ffn_dim=args.lam_ffn_dim,
    96	        latent_action_dim=args.latent_action_dim,
    97	        num_actions=args.num_actions,
    98	        lam_patch_size=args.lam_patch_size,
    99	        lam_num_blocks=args.lam_num_blocks,
   100	        lam_num_heads=args.lam_num_heads,
   101	        lam_co_train=False,
   102	        use_gt_actions=args.use_gt_actions,
   103	        # Dynamics
   104	        dyna_dim=args.dyna_dim,
   105	        dyna_ffn_dim=args.dyna_ffn_dim,
   106	        dyna_num_blocks=args.dyna_num_blocks,
   107	        dyna_num_heads=args.dyna_num_heads,
   108	        param_dtype=args.param_dtype,
   109	        dtype=args.dtype,
   110	        use_flash_attention=args.use_flash_attention,
   111	        diffusion_denoise_steps=args.diffusion_denoise_steps,
   112	        # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113	        decode=False,
   114	        rngs=rngs,
   115	    )
   116	
   117	    # Need to delete lam decoder for checkpoint loading
   118	    if not args.use_gt_actions:
   119	        assert genie.lam is not None
   120	        del genie.lam.decoder
   121	
   122	    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123	    handler_registry.add(
   124	        \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125	    )
   126	    handler_registry.add(
   127	        \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128	    )
   129	    checkpoint_options = ocp.CheckpointManagerOptions(
   130	        step_format_fixed_length=6,
   131	    )
   132	    checkpoint_manager = ocp.CheckpointManager(
   133	        args.checkpoint,
   134	        options=checkpoint_options,
   135	        handler_registry=handler_registry,
   136	    )
   137	
   138	    dummy_tx = optax.adamw(
   139	        learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140	        b1=0.9,
   141	        b2=0.9,
   142	        weight_decay=1e-4,
   143	        mu_dtype=args.dtype,
   144	    )
   145	    dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146	
   147	    abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148	    abstract_optimizer_state = nnx.state(abstract_optimizer)
   149	    restored = checkpoint_manager.restore(
   150	        checkpoint_manager.latest_step(),
   151	        args=ocp.args.Composite(
   152	            model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153	        ),
   154	    )
   155	    restored_optimizer_state = restored[\"model_state\"]
   156	    nnx.update(dummy_optimizer, restored_optimizer_state)
   157	
   158	    # --- Define sampling function ---
   159	    def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160	        \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161	        frames = model.sample(
   162	            batch,
   163	            args.seq_len,
   164	            args.diffusion_denoise_steps,
   165	            args.diffusion_corrupt_context_factor,
   166	        )
   167	        return frames
   168	
   169	    # --- Define autoregressive sampling loop ---
   170	    def _autoreg_sample(
   171	        genie: GenieDiffusion, rng: jax.Array, batch: dict
   172	    ) -> jax.Array:
   173	        batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174	        batch[\"rng\"] = rng
   175	        generated_vid_BSHWC = _sampling_fn(genie, batch)
   176	        return generated_vid_BSHWC
   177	
   178	    # --- Get video + latent actions ---
   179	    array_record_files = [
   180	        os.path.join(args.data_dir, x)
   181	        for x in os.listdir(args.data_dir)
   182	        if x.endswith(\".array_record\")
   183	    ]
   184	    dataloader = get_dataloader(
   185	        array_record_files,
   186	        args.seq_len,
   187	        args.batch_size,
   188	        args.image_height,
   189	        args.image_width,
   190	        args.image_channels,
   191	        # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192	        num_workers=0,
   193	        prefetch_buffer_size=1,
   194	        seed=args.seed,
   195	    )
   196	    dataloader = iter(dataloader)
   197	    batch = next(dataloader)
   198	    gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199	    print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200	    batch[\"videos\"] = gt_video.astype(args.dtype)
   201	    # Get latent actions for all videos in the batch
   202	    action_batch_E = None
   203	    if not args.use_gt_actions:
   204	        action_batch_E = genie.vq_encode(batch, training=False)
   205	        batch[\"latent_actions\"] = action_batch_E
   206	        print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207	
   208	    # --- Sample + evaluate video ---
   209	    recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211	    recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212	    print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213	
   214	    gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215	    print(f\"DEBUG: gt shape: {gt.shape}\")
   216	    recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217	    print(f\"DEBUG: recon shape: {recon.shape}\")
   218	
   219	    ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220	    psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221	    ssim = jnp.asarray(ssim_vmap(gt, recon))
   222	    psnr = jnp.asarray(psnr_vmap(gt, recon))
   223	    per_frame_ssim = ssim.mean(0)
   224	    per_frame_psnr = psnr.mean(0)
   225	    avg_ssim = ssim.mean()
   226	    avg_psnr = psnr.mean()
   227	
   228	    print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229	    print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230	
   231	    print(f\"SSIM: {avg_ssim}\")
   232	    print(f\"PSNR: {avg_psnr}\")
   233	
   234	    # --- Construct video ---
   235	    true_videos = (gt_video * 255).astype(np.uint8)
   236	    pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237	    video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238	    video_comparison[0] = true_videos[:, : args.seq_len]
   239	    video_comparison[1] = pred_videos
   240	    frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241	
   242	    # --- Save video ---
   243	    imgs = [Image.fromarray(img) for img in frames]
   244	    # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245	    B = batch[\"videos\"].shape[0]
   246	    if action_batch_E is not None:
   247	        action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248	    else:
   249	        action_batch_BSm11 = jnp.reshape(
   250	            batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251	        )
   252	    for t, img in enumerate(imgs[1:]):
   253	        d = ImageDraw.Draw(img)
   254	        for row in range(B):
   255	            if args.print_action_indices:
   256	                action = action_batch_BSm11[row, t, 0]
   257	                y_offset = row * batch[\"videos\"].shape[2] + 2
   258	                d.text((2, y_offset), f\"{action}\", fill=255)
   259	
   260	    os.makedirs(args.output_dir, exist_ok=True)
   261	    imgs[0].save(
   262	        os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263	        save_all=True,
   264	        append_images=imgs[1:],
   265	        duration=250,
   266	        loop=0,
   267	    )
   268	    # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269	    skip = 4
   270	    # pred_videos: (B, T, H, W, C)
   271	    B, T, H, W, C = pred_videos.shape
   272	
   273	    for i in range(B):
   274	        # Predicted
   275	        pred_strip = np.concatenate(
   276	            [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277	        )  # resulting shape: (H, (T-skip)*W, C)
   278	        pred_img = Image.fromarray(pred_strip)
   279	
   280	        # Write actions on each frame for the predicted strip
   281	        if args.print_action_indices:
   282	            d = ImageDraw.Draw(pred_img)
   283	            for t in range(skip, args.seq_len):
   284	                if action_batch_E is not None:
   285	                    action = int(action_batch_BSm11[i, t - 1, 0])
   286	                else:
   287	                    action = int(batch[\"actions\"][i, t - 1])
   288	                x_offset = (t - skip) * pred_videos.shape[3] + 2
   289	                y_offset = 2
   290	                d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291	
   292	        pred_img.save(
   293	            os.path.join(
   294	                args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295	            )
   296	        )
   297	        # GT
   298	        gt_strip = np.concatenate(
   299	            [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300	        )  # resulting shape: (H, (T-skip)*W, C)
   301	        gt_img = Image.fromarray(gt_strip)
   302	        gt_img.save(
   303	            os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304	        )
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```"}, {"role": "user", "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '205,205d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '205,216p'
```"}, {"role": "user", "content": "<stdout>
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   209      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211
   212      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: gt shape: {gt.shape}\")
   214      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: recon shape: {recon.shape}\")
   216
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '208,208d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '209,209d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/0", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/1", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/2", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/3", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/4", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/5", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/6", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/7", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/8", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/9", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/10", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_replace/11", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=32
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_32_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=1536 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-32-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 32-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/0", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/1", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/2", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/3", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/4", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/5", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/6", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/7", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/8", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/9", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/10", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_file_switch/11", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```"}, {"role": "user", "content": "<stdout>
total 144
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1322 Dec  3 14:23 train_tokenizer_16_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1309 Dec  3 14:23 train_tokenizer_1_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1319 Dec  3 14:23 train_tokenizer_2_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1323 Dec  3 14:23 train_tokenizer_32_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1310 Dec  3 14:23 train_tokenizer_4_nodes.sbatch
-rw-r--r-- 1 mihir.mahajan mihir.mahajan 1318 Dec  3 14:23 train_tokenizer_8_nodes.sbatch
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=1
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=48 \\
    33      --min_lr=0 \\
    34      --max_lr=1e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<EVAL>
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```"}, {"role": "user", "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=32
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_32_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=1536 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-32-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 32-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/0", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/1", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/2", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/3", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/4", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/5", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/6", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/7", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/8", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/9", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/10", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/11", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/12", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '134,134c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/13", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '134,134c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '188,199p'
```"}, {"role": "user", "content": "<stdout>
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/14", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '134,134c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '188,199p'
```"}, {"role": "user", "content": "<stdout>
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '193,193c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/15", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '134,134c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '188,199p'
```"}, {"role": "user", "content": "<stdout>
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '193,193c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '194,194c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/16", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '134,134c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '188,199p'
```"}, {"role": "user", "content": "<stdout>
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '193,193c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '194,194c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '202,213p'
```"}, {"role": "user", "content": "<stdout>
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
</stdout>"}], "expected_final_response": ""}
{"task_id": "eval_cursor_movement/17", "context": [{"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
     1  from typing import Dict
     2
     3  import jax
     4  import jax.numpy as jnp
     5  import flax.nnx as nnx
     6
     7  from jasmine.utils.nn import AxialTransformer
     8
     9
    10  class DynamicsMaskGIT(nnx.Module):
    11      \"\"\"
    12      MaskGIT dynamics model
    13
    14      Dimension keys:
    15          B: batch size
    16          T: sequence length
    17          N: number of patches per frame
    18          L: latent dimension
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
    31          mask_limit: float,
    32          param_dtype: jnp.dtype,
    33          dtype: jnp.dtype,
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
    46          self.param_dtype = param_dtype
    47          self.dtype = dtype
    48          self.use_flash_attention = use_flash_attention
    49          self.decode = decode
    50          self.transformer = AxialTransformer(
    51              self.model_dim,
    52              self.model_dim,
    53              self.ffn_dim,
    54              self.num_latents,
    55              self.num_blocks,
    56              self.num_heads,
    57              self.dropout,
    58              self.param_dtype,
    59              self.dtype,
    60              decode=self.decode,
    61              use_flash_attention=self.use_flash_attention,
    62              spatial_causal=False,
    63              temporal_causal=True,
    64              rngs=rngs,
    65          )
    66          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
    67          self.mask_token = nnx.Param(
    68              nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))
    69          )
    70          self.action_up = nnx.Linear(
    71              self.latent_action_dim,
    72              self.model_dim,
    73              param_dtype=self.param_dtype,
    74              dtype=self.dtype,
    75              rngs=rngs,
    76          )
    77
    78      def __call__(
    79          self,
    80          batch: Dict[str, jax.Array],
    81      ) -> tuple[jax.Array, jax.Array]:
    82          # --- Mask videos ---
    83          video_tokens_BTN = batch[\"video_tokens\"]
    84          latent_actions_BTm11L = batch[\"latent_actions\"]
    85          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
    86
    87          batch_size = vid_embed_BTNM.shape[0]
    88          _rng_prob, *_rngs_mask = jax.random.split(batch[\"mask_rng\"], batch_size + 1)
    89          mask_prob = jax.random.uniform(
    90              _rng_prob, shape=(batch_size,), minval=self.mask_limit
    91          )
    92          per_sample_shape = vid_embed_BTNM.shape[1:-1]
    93          mask = jax.vmap(
    94              lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),
    95              in_axes=(0, 0),
    96          )(jnp.asarray(_rngs_mask), mask_prob)
    97          mask = mask.at[:, 0].set(False)
    98          vid_embed_BTNM = jnp.where(
    99              jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM
   100          )
   101
   102          # --- Predict transition ---
   103          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   104          padded_act_embed_BT1M = jnp.pad(
   105              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   106          )
   107          vid_embed_BTNp1M = jnp.concatenate(
   108              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   109          )
   110          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   111          logits_BTNV = logits_BTNp1V[:, :, 1:]
   112          return logits_BTNV, mask
   113
   114
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
   127          param_dtype: jnp.dtype,
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133          self.model_dim = model_dim
   134          self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
   140          self.param_dtype = param_dtype
   141          self.dtype = dtype
   142          self.use_flash_attention = use_flash_attention
   143          self.decode = decode
   144          self.transformer = AxialTransformer(
   145              self.model_dim,
   146              self.model_dim,
   147              self.ffn_dim,
   148              self.num_latents,
   149              self.num_blocks,
   150              self.num_heads,
   151              self.dropout,
   152              self.param_dtype,
   153              self.dtype,
   154              use_flash_attention=self.use_flash_attention,
   155              spatial_causal=True,
   156              temporal_causal=True,
   157              rngs=rngs,
   158              decode=self.decode,
   159          )
   160          self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)
   161          self.action_up = nnx.Linear(
   162              self.latent_action_dim,
   163              self.model_dim,
   164              param_dtype=self.param_dtype,
   165              dtype=self.dtype,
   166              rngs=rngs,
   167          )
   168
   169      def __call__(
   170          self,
   171          batch: Dict[str, jax.Array],
   172      ) -> tuple[jax.Array, jax.Array]:
   173          video_tokens_BTN = batch[\"video_tokens\"]
   174          latent_actions_BTm11L = batch[\"latent_actions\"]
   175          vid_embed_BTNM = self.patch_embed(video_tokens_BTN)
   176          act_embed_BTm11M = self.action_up(latent_actions_BTm11L)
   177          padded_act_embed_BT1M = jnp.pad(
   178              act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))
   179          )
   180          vid_embed_BTNp1M = jnp.concatenate(
   181              [padded_act_embed_BT1M, vid_embed_BTNM], axis=2
   182          )
   183          logits_BTNp1V = self.transformer(vid_embed_BTNp1M)
   184          logits_BTNV = logits_BTNp1V[:, :, :-1]
   185          return logits_BTNV, jnp.ones_like(video_tokens_BTN)
   186
   187
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
   200          dropout: float,
   201          param_dtype: jnp.dtype,
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
   214          self.param_dtype = param_dtype
   215          self.dtype = dtype
   216          self.use_flash_attention = use_flash_attention
   217          self.denoise_steps = denoise_steps
   218          self.decode = decode
   219          self.diffusion_transformer = AxialTransformer(
   220              self.latent_patch_dim,
   221              self.model_dim,
   222              self.ffn_dim,
   223              self.latent_patch_dim,
   224              self.num_blocks,
   225              self.num_heads,
   226              self.dropout,
   227              self.param_dtype,
   228              self.dtype,
   229              use_flash_attention=self.use_flash_attention,
   230              spatial_causal=False,
   231              temporal_causal=True,
   232              rngs=rngs,
   233              decode=self.decode,
   234          )
   235          self.action_up = nnx.Linear(
   236              self.latent_action_dim,
   237              self.latent_patch_dim,
   238              param_dtype=self.param_dtype,
   239              dtype=self.dtype,
   240              rngs=rngs,
   241          )
   242          self.timestep_embed = nnx.Embed(
   243              num_embeddings=self.denoise_steps,
   244              features=self.latent_patch_dim,
   245              param_dtype=self.param_dtype,
   246              dtype=self.dtype,
   247              rngs=rngs,
   248          )
   249
   250      def __call__(
   251          self,
   252          batch: Dict[str, jax.Array],
   253      ) -> tuple[jax.Array, jax.Array]:
   254          # Code adapted from https://github.com/kvfrans/shortcut-models/blob/main/baselines/targets_naive.py
   255          _rng_time, _rng_noise = jax.random.split(batch[\"rng\"])
   256          latents_BTNL = batch[\"token_latents\"]
   257          latent_actions_BTm11L = batch[\"latent_actions\"]
   258          B, T, N, L = latents_BTNL.shape
   259
   260          # --- Add noise to latents ---
   261          denoise_step_BT = jax.random.randint(
   262              _rng_time, (B, T), minval=0, maxval=self.denoise_steps
   263          )
   264          denoise_step_embed_BT1L = self.timestep_embed(denoise_step_BT).reshape(
   265              B, T, 1, self.latent_patch_dim
   266          )
   267          denoise_t_BT = denoise_step_BT / self.denoise_steps
   268          denoise_t_BT11 = denoise_t_BT[:, :, jnp.newaxis, jnp.newaxis]
   269          noise_BTNL = jax.random.normal(_rng_noise, (B, T, N, L))
   270          noised_latents_BTNL = (
   271              1
   272              - (1 - 1e-5) * denoise_t_BT11  # we adopt 1e-5 from kvfrans/shortcut-models
   273          ) * noise_BTNL + denoise_t_BT11 * latents_BTNL
   274
   275          # --- Process actions ---
   276          act_embed_BTm11L = self.action_up(latent_actions_BTm11L)
   277          padded_act_embed_BT1L = jnp.pad(
   278              act_embed_BTm11L, ((0, 0), (1, 0), (0, 0), (0, 0))
   279          )
   280
   281          # --- Call the diffusion transformer ---
   282          inputs_BTNp2L = jnp.concatenate(
   283              [padded_act_embed_BT1L, denoise_step_embed_BT1L, noised_latents_BTNL],
   284              axis=2,
   285          )
   286          outputs_BTNp2L = self.diffusion_transformer(
   287              inputs_BTNp2L,
   288          )
   289          pred_latents_BTNL = outputs_BTNp2L[:, :, 2:]
   290          return pred_latents_BTNL, denoise_t_BT
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '19,30p'
```"}, {"role": "user", "content": "<stdout>
    19          V: vocabulary size (number of latents)
    20      \"\"\"
    21
    22      def __init__(
    23          self,
    24          model_dimension: int,
    25          ffn_dimension: int,
    26          num_latents: int,
    27          latent_action_dim: int,
    28          num_blocks: int,
    29          num_heads: int,
    30          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '24,24c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '25,25c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '34,45p'
```"}, {"role": "user", "content": "<stdout>
    34          use_flash_attention: bool,
    35          rngs: nnx.Rngs,
    36          decode: bool,
    37      ):
    38          self.model_dim = model_dimension
    39          self.ffn_dim = ffn_dimension
    40          self.num_latents = num_latents
    41          self.latent_action_dim = latent_action_dim
    42          self.num_blocks = num_blocks
    43          self.num_heads = num_heads
    44          self.dropout = dropout
    45          self.mask_limit = mask_limit
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '38,38c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '39,39c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '115,126p'
```"}, {"role": "user", "content": "<stdout>
   115  class DynamicsCausal(nnx.Module):
   116      \"\"\"Causal dynamics model\"\"\"
   117
   118      def __init__(
   119          self,
   120          model_dimension: int,
   121          ffn_dimension: int,
   122          num_latents: int,
   123          latent_action_dim: int,
   124          num_blocks: int,
   125          num_heads: int,
   126          dropout: float,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '120,120c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '121,121c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '128,139p'
```"}, {"role": "user", "content": "<stdout>
   128          dtype: jnp.dtype,
   129          use_flash_attention: bool,
   130          rngs: nnx.Rngs,
   131          decode: bool,
   132      ):
   133                  self.model_dim = model_dim
   134                  self.ffn_dim = ffn_dim
   135          self.num_latents = num_latents
   136          self.latent_action_dim = latent_action_dim
   137          self.num_blocks = num_blocks
   138          self.num_heads = num_heads
   139          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '134,134c\\\	\	self.ffn_dim = ffn_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '188,199p'
```"}, {"role": "user", "content": "<stdout>
   188  class DynamicsDiffusion(nnx.Module):
   189      \"\"\"Diffusion transformer dynamics model\"\"\"
   190
   191      def __init__(
   192          self,
   193          model_dimension: int,
   194          ffn_dimension: int,
   195          latent_patch_dim: int,
   196          latent_action_dim: int,
   197          num_blocks: int,
   198          num_heads: int,
   199          denoise_steps: int,
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '193,193c\\\	\	model_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '194,194c\\\	\	ffn_dim: int,' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}, {"role": "assistant", "content": "<EVAL>
```bash
cat -n jasmine/models/dynamics.py | sed -n '202,213p'
```"}, {"role": "user", "content": "<stdout>
   202          dtype: jnp.dtype,
   203          use_flash_attention: bool,
   204          rngs: nnx.Rngs,
   205          decode: bool,
   206      ):
   207          self.model_dim = model_dimension
   208          self.ffn_dim = ffn_dimension
   209          self.latent_patch_dim = latent_patch_dim
   210          self.latent_action_dim = latent_action_dim
   211          self.num_blocks = num_blocks
   212          self.num_heads = num_heads
   213          self.dropout = dropout
</stdout>"}, {"role": "assistant", "content": "<NO_EVAL>
```bash
sed -i '133,133c\\\	\	self.model_dim = model_dim' jasmine/models/dynamics.py
```"}, {"role": "user", "content": "<stdout>
</stdout>"}], "expected_final_response": ""}